Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 80710

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 88403

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-12-09T22:47:01+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

病床データ

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`) %>% 
  dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}

　

対策ダッシュボード

NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。

"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>% 
  readr::read_csv(guess_max = 12500) %>% 
  dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
                confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
                `入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
                `確認中`) %>% 
  dplyr::mutate(date = lubridate::as_date(date)) %>% 
  dplyr::mutate_if(is.numeric, .funs = as.integer)

　

新型コロナ関連ニュース

新型コロナ関連のニュース

news <- "https://gist.githubusercontent.com/k-metrics/76fea197fa32466a2f99ff59f721b98a/raw/4c971a6cde2033e458525b793e832c4a87cbae2d/covid19_news.csv" %>% 
    readr::read_csv() %>% 
  dplyr::filter(area == "日本")
news

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	171688
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	169403	0
dateAnnounced	0	1.00	10	10	0	316	0
gender	60970	0.64	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	167143	0.03	8	23	0	8	0
notes	93775	0.45	1	270	0	74934	1
mhlwPatientNumber	171239	0.00	1	11	0	434	0
prefecturePatientNumber	55687	0.68	5	20	0	115992	0
prefectureSourceURL	140288	0.18	5	224	0	3455	0
residence	69058	0.60	1	38	0	1429	0
sourceURL	1207	0.99	1	239	0	9466	0
relatedPatients	159101	0.07	2	259	0	7470	0
knownCluster	169154	0.01	3	88	0	235	0
detectedCityTown	142655	0.17	2	22	0	667	0
cityPrefectureNumber	142956	0.17	1	34	0	28723	2
citySourceURL	159455	0.07	7	317	0	3695	0
deceasedDate	169302	0.01	10	10	0	266	0
deceasedReportedDate	170464	0.01	10	62	0	208	0
deathSourceURL	170609	0.01	14	123	0	658	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 169402, FAL: 2286
charterFlightPassenger	171674	0	1.00	TRU: 14
cruisePassengerDisembarked	171677	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	24.13	24.99	-1	-1	20	40	100	▇▆▃▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	167757
Number of columns	18
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	8	0	167757	0
knownCluster	165273	0.01	3	88	0	232	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-12-09	2020-10-03	313
deceasedDate	167378	0	2020-02-13	2020-11-19	2020-05-08	150
deceasedReportedDate	167428	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	58642	0.65	FALSE	2	M: 61012, F: 48103
patientStatus	165246	0.01	FALSE	8	Hos: 1246, Dec: 371, Hom: 315, Dis: 276
ageBracket	0	1.00	FALSE	13	-1: 58727, 20: 29297, 30: 18917, 40: 15988
pcode	0	1.00	FALSE	47	13: 44976, 27: 23421, 14: 14128, 23: 11894
pref	0	1.00	FALSE	47	東京都: 44976, 大阪府: 23421, 神奈川: 14128, 愛知県: 11894
region	0	1.00	FALSE	8	関東地: 80710, 近畿地: 37025, 中部地: 18613, 九州地: 14446
広域圏	15213	0.91	FALSE	8	首都圏: 81147, 近畿圏: 36040, 中部圏: 17117, 九州圏: 9786
通俗的区分	0	1.00	FALSE	11	関東: 80710, 関西: 36040, 東海: 16255, 北海道: 10553
fct_pref	0	1.00	FALSE	47	Tok: 44976, Osa: 23421, Kan: 14128, Aic: 11894

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 167757
charterFlightPassenger	167750	0	1.00	TRU: 7
cruisePassengerDisembarked	167746	0	1.00	TRU: 11
cluster	0	1	0.01	FAL: 165273, TRU: 2484

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.33）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.33）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

# 祝日ファイルは以下をダウンロードしておく（SSLがエラーになるので自動処理できない）
# "https://www8.cao.go.jp/chosei/shukujitsu/syukujitsu.csv"

# 祝日判定関数
source("https://raw.githubusercontent.com/logics-of-blue/website/master/010_forecast/20190714_R%E8%A8%80%E8%AA%9E%E3%81%AB%E3%81%8A%E3%81%91%E3%82%8B%E6%97%A5%E6%9C%AC%E3%81%AE%E7%A5%9D%E6%97%A5%E5%88%A4%E5%AE%9A/jholiday.R", encoding="utf-8")

sec_scale <- 100

emergency <- news %>% 
  dplyr::filter(category == "緊急事態")

goto <- news %>% 
  dplyr::filter(category == "GoTo")

x_by_all %>% 
  dplyr::mutate(hday = is.jholiday(target_date = date,
                                   holiday_source = "./Covid19/syukujitsu.csv"),
                wday = lubridate::wday(date, week_start = 1),
                wday = dplyr::if_else(wday > 5, TRUE, FALSE),
                wday = dplyr::if_else(hday | wday, 3000L,  NA_integer_)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = wday), stat = "identity", width = 1.0,
                      fill = "red", alpha = 0.1) + 
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = emergency,
                        colour = "dark blue", linetype = "dashed", size = 0.15) +
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = goto,
                        colour = "magenta", linetype = "dashed", size = 0.25) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      fill = "dark gray", alpha = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2500, label = news),
                              size = 2.0, data = emergency) +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2250, label = news),
                              size = 2.5, data = goto, colour = "magenta") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

死亡者の日次集計

厚生労働省のデータと乖離がある。 　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_prefs %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

sec_scale <- 50
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n), fill = "dark gray", stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), colour = "dark green",
                       linetype = "dashed", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale), colour = "dark green") +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・同移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計死亡者数（実線）")
    )

　

年代別

x_by_age <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9"))
x_by_age

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(rate = round((n / sum(n) * 100), 1))

　

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = ageBracket, values_from = n)

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() +
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) +
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

都道府県別

g_age_by_pref <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::group_by(pref, ageBracket, cluster) %>%
  # dplyr::group_by(pref, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") +
    # ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref

g_age_by_pref + 
  ggplot2::facet_grid(~ cluster)

g_age_by_pref_wo <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(pref, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref_wo

g_age_by_pref_wo + 
  ggplot2::facet_grid(~ cluster) + 
  ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime))

　

Visualize

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 205.9218 199.6235 175.0601 198.0492 177.8336 203.0770 201.8914 205.8220
##  [9] 199.6318 196.8826 200.1114 201.1730 201.1730 201.1730
## 
## $北海道地方$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 191.4753 183.8278
## 48.28571 183.1210 174.3850
## 48.42857 154.9530 144.3090
## 48.57143 177.3466 166.3873
## 48.71429 155.8807 144.2595
## 48.85714 179.2835 166.6880
## 49.00000 176.3898 162.8901
## 49.14286 175.7995 159.9066
## 49.28571 166.9720 149.6829
## 49.42857 161.1577 142.2461
## 49.57143 162.5960 142.7366
## 49.71429 161.6152 140.6746
## 49.85714 159.4037 137.2924
## 50.00000 157.3035 134.0804
## 
## $北海道地方$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 220.3684 228.0159
## 48.28571 216.1260 224.8620
## 48.42857 195.1673 205.8113
## 48.57143 218.7517 229.7110
## 48.71429 199.7864 211.4076
## 48.85714 226.8705 239.4660
## 49.00000 227.3930 240.8927
## 49.14286 235.8444 251.7373
## 49.28571 232.2916 249.5807
## 49.42857 232.6076 251.5192
## 49.57143 237.6267 257.4861
## 49.71429 240.7307 261.6713
## 49.85714 242.9422 265.0535
## 50.00000 245.0424 268.2655
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 52.38790 44.32229 48.03472 49.71490 43.54187 47.55907 53.21195 50.65908
##  [9] 46.30589 49.08797 47.95432 48.45804 48.79920 48.45709
## 
## $東北地方$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 44.96947 41.04239
## 48.28571 36.45972 32.29752
## 48.42857 40.08926 35.88318
## 48.57143 41.71467 37.47961
## 48.71429 35.28191 30.90935
## 48.85714 39.28011 34.89750
## 49.00000 44.67491 40.15568
## 49.14286 41.71845 36.98557
## 49.28571 37.02204 32.10747
## 49.42857 39.71293 34.75008
## 49.57143 38.40569 33.35096
## 49.71429 38.75005 33.61095
## 49.85714 38.96755 33.76299
## 50.00000 38.44874 33.15064
## 
## $東北地方$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 59.80634 63.73342
## 48.28571 52.18486 56.34705
## 48.42857 55.98019 60.18626
## 48.57143 57.71513 61.95020
## 48.71429 51.80183 56.17438
## 48.85714 55.83802 60.22063
## 49.00000 61.74899 66.26823
## 49.14286 59.59971 64.33260
## 49.28571 55.58974 60.50431
## 49.42857 58.46302 63.42587
## 49.57143 57.50294 62.55768
## 49.71429 58.16602 63.30512
## 49.85714 58.63085 63.83541
## 50.00000 58.46543 63.76353
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 1191.7787 1128.5497 1146.0448  864.3546  663.5468  848.0511 1097.5936
##  [8] 1188.5186 1162.4309 1206.0834  934.5242  729.6688  901.7640 1137.1017
## 
## $関東地方$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%       95%
## 48.14286 1114.7839 1074.0253
## 48.28571 1032.9863  982.3980
## 48.42857 1043.4226  989.0977
## 48.57143  758.7529  702.8508
## 48.71429  555.9514  498.9938
## 48.85714  738.0509  679.8203
## 49.00000  983.9678  923.8180
## 49.14286 1061.0452  993.5648
## 49.28571 1023.4966  949.9493
## 49.42857 1058.3238  980.1045
## 49.57143  780.0630  698.2963
## 49.71429  569.8933  485.3133
## 49.85714  737.4120  650.4093
## 50.00000  968.4595  879.1857
## 
## $関東地方$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%       95%
## 48.14286 1268.7735 1309.5321
## 48.28571 1224.1131 1274.7013
## 48.42857 1248.6671 1302.9920
## 48.57143  969.9562 1025.8584
## 48.71429  771.1422  828.0998
## 48.85714  958.0514 1016.2820
## 49.00000 1211.2193 1271.3692
## 49.14286 1315.9921 1383.4724
## 49.28571 1301.3651 1374.9124
## 49.42857 1353.8431 1432.0624
## 49.57143 1088.9853 1170.7520
## 49.71429  889.4443  974.0244
## 49.85714 1066.1161 1153.1188
## 50.00000 1305.7439 1395.0177
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 383.6448 382.6124 379.6831 293.9977 233.7426 303.5046 363.7805 388.9488
##  [9] 390.9050 403.5108 330.3551 275.6432 352.0576 408.8756
## 
## $中部地方$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 359.9337 347.3818
## 48.28571 353.1483 337.5510
## 48.42857 344.7565 326.2674
## 48.57143 256.6755 236.9184
## 48.71429 194.5559 173.8117
## 48.85714 261.6431 239.4830
## 49.00000 319.8075 296.5297
## 49.14286 337.5370 310.3213
## 49.28571 332.9333 302.2450
## 49.42857 339.5567 305.7014
## 49.57143 260.9957 224.2791
## 49.71429 201.7560 162.6424
## 49.85714 274.1174 232.8583
## 50.00000 327.3083 284.1292
## 
## $中部地方$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 407.3559 419.9078
## 48.28571 412.0764 427.6738
## 48.42857 414.6097 433.0987
## 48.57143 331.3198 351.0770
## 48.71429 272.9292 293.6734
## 48.85714 345.3661 367.5263
## 49.00000 407.7534 431.0313
## 49.14286 440.3605 467.5762
## 49.28571 448.8766 479.5649
## 49.42857 467.4649 501.3201
## 49.57143 399.7145 436.4312
## 49.71429 349.5305 388.6440
## 49.85714 429.9979 471.2570
## 50.00000 490.4429 533.6220
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 726.1641 671.2777 707.1999 617.5883 506.1933 587.8394 730.9290 740.9562
##  [9] 707.5478 738.3221 661.5526 566.1213 636.0669 758.6507
## 
## $近畿地方$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 679.8555 655.3412
## 48.28571 615.6389 586.1855
## 48.42857 648.7855 617.8628
## 48.57143 556.5243 524.1990
## 48.71429 442.5900 408.9204
## 48.85714 521.7944 486.8322
## 49.00000 662.5292 626.3206
## 49.14286 661.4513 619.3639
## 49.28571 621.4914 575.9359
## 49.42857 648.2698 600.5989
## 49.57143 567.6742 517.9780
## 49.71429 468.5668 416.9246
## 49.85714 534.9700 481.4525
## 50.00000 654.1312 598.8019
## 
## $近畿地方$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 772.4727 796.9870
## 48.28571 726.9164 756.3698
## 48.42857 765.6143 796.5370
## 48.57143 678.6523 710.9777
## 48.71429 569.7967 603.4662
## 48.85714 653.8845 688.8467
## 49.00000 799.3287 835.5373
## 49.14286 820.4612 862.5486
## 49.28571 793.6042 839.1597
## 49.42857 828.3745 876.0453
## 49.57143 755.4309 805.1272
## 49.71429 663.6757 715.3180
## 49.85714 737.1639 790.6814
## 50.00000 863.1701 918.4994
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 74.72875 71.65043 71.86848 72.08653 72.30458 72.52262 72.74067 72.95872
##  [9] 73.17676 73.39481 73.61286 73.83090 74.04895 74.26700
## 
## $中国地方$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 64.51973 59.11541
## 48.28571 60.54023 54.65884
## 48.42857 60.28799 54.15765
## 48.57143 60.05412 53.68455
## 48.71429 59.83663 53.23649
## 48.85714 59.63384 52.81093
## 49.00000 59.44436 52.40572
## 49.14286 59.26701 52.01906
## 49.28571 59.10076 51.64938
## 49.42857 58.94473 51.29532
## 49.57143 58.79814 50.95570
## 49.71429 58.66031 50.62949
## 49.85714 58.53064 50.31575
## 50.00000 58.40860 50.01367
## 
## $中国地方$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 84.93778 90.34210
## 48.28571 82.76064 88.64203
## 48.42857 83.44897 89.57931
## 48.57143 84.11893 90.48850
## 48.71429 84.77253 91.37266
## 48.85714 85.41141 92.23432
## 49.00000 86.03698 93.07562
## 49.14286 86.65042 93.89837
## 49.28571 87.25276 94.70415
## 49.42857 87.84489 95.49430
## 49.57143 88.42757 96.27001
## 49.71429 89.00150 97.03232
## 49.85714 89.56726 97.78215
## 50.00000 90.12540 98.52033
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 24.51443 24.75186 25.21646 25.66890 25.12494 23.50329 25.47221 25.24493
##  [9] 25.09614 25.09614 25.09614 25.09614 25.09614 25.09614
## 
## $四国地方$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 20.42644 18.26238
## 48.28571 20.09549 17.63055
## 48.42857 20.27383 17.65735
## 48.57143 20.45570 17.69599
## 48.71429 19.65454 16.75868
## 48.85714 17.78725 14.76136
## 49.00000 19.52065 16.37009
## 49.14286 19.19001 15.98473
## 49.28571 18.89011 15.60483
## 49.42857 18.72587 15.35364
## 49.57143 18.56575 15.10877
## 49.71429 18.40947 14.86976
## 49.85714 18.25676 14.63620
## 50.00000 18.10738 14.40775
## 
## $四国地方$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 28.60243 30.76648
## 48.28571 29.40824 31.87318
## 48.42857 30.15910 32.77558
## 48.57143 30.88210 33.64181
## 48.71429 30.59534 33.49120
## 48.85714 29.21934 32.24523
## 49.00000 31.42376 34.57432
## 49.14286 31.29984 34.50512
## 49.28571 31.30217 34.58745
## 49.42857 31.46642 34.83864
## 49.57143 31.62653 35.08351
## 49.71429 31.78282 35.32253
## 49.85714 31.93553 35.55608
## 50.00000 32.08490 35.78453
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 174.9710 160.7617 158.2698 174.7484 175.9406 196.4126 197.7060 186.5740
##  [9] 185.4028 180.8234 191.5678 191.5305 201.2913 201.9845
## 
## $九州地方$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%       95%
## 48.14286 151.4488 138.99694
## 48.28571 132.0640 116.87238
## 48.42857 126.1315 109.11846
## 48.57143 141.8321 124.40726
## 48.71429 141.5186 123.29666
## 48.85714 158.6350 138.63667
## 49.00000 156.8473 135.21807
## 49.14286 139.7662 114.98765
## 49.28571 134.2433 107.16106
## 49.42857 125.8796  96.79415
## 49.57143 133.8235 103.25555
## 49.71429 130.8405  98.71310
## 49.85714 137.2717 103.38172
## 50.00000 134.8000  99.23473
## 
## $九州地方$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 198.4931 210.9450
## 48.28571 189.4593 204.6510
## 48.42857 190.4082 207.4212
## 48.57143 207.6647 225.0895
## 48.71429 210.3626 228.5845
## 48.85714 234.1903 254.1885
## 49.00000 238.5646 260.1938
## 49.14286 233.3818 258.1603
## 49.28571 236.5623 263.6445
## 49.42857 235.7672 264.8527
## 49.57143 249.3121 279.8801
## 49.71429 252.2205 284.3479
## 49.85714 265.3110 299.2009
## 50.00000 269.1690 304.7343

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 205.9218 199.6235 175.0601 198.0492 177.8336 203.0770 201.8914 205.8220
##  [9] 199.6318 196.8826 200.1114 201.1730 201.1730 201.1730
## 
## $北海道$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 191.4753 183.8278
## 48.28571 183.1210 174.3850
## 48.42857 154.9530 144.3090
## 48.57143 177.3466 166.3873
## 48.71429 155.8807 144.2595
## 48.85714 179.2835 166.6880
## 49.00000 176.3898 162.8901
## 49.14286 175.7995 159.9066
## 49.28571 166.9720 149.6829
## 49.42857 161.1577 142.2461
## 49.57143 162.5960 142.7366
## 49.71429 161.6152 140.6746
## 49.85714 159.4037 137.2924
## 50.00000 157.3035 134.0804
## 
## $北海道$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 220.3684 228.0159
## 48.28571 216.1260 224.8620
## 48.42857 195.1673 205.8113
## 48.57143 218.7517 229.7110
## 48.71429 199.7864 211.4076
## 48.85714 226.8705 239.4660
## 49.00000 227.3930 240.8927
## 49.14286 235.8444 251.7373
## 49.28571 232.2916 249.5807
## 49.42857 232.6076 251.5192
## 49.57143 237.6267 257.4861
## 49.71429 240.7307 261.6713
## 49.85714 242.9422 265.0535
## 50.00000 245.0424 268.2655
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 9.161090 6.242487 8.218009 6.880833 7.785930 7.173295 7.587971 7.307288
##  [9] 7.497274 7.368678 7.455721 7.396804 7.436684 7.409690
## 
## $青森県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%       95%
## 48.14286 6.484139 5.0670462
## 48.28571 3.164319 1.5348360
## 48.42857 5.057734 3.3847860
## 48.57143 3.478709 1.6777340
## 48.71429 4.268937 2.4071539
## 48.85714 3.475909 1.5186314
## 49.00000 3.764939 1.7411488
## 49.14286 3.332925 1.2290237
## 49.28571 3.396837 1.2261972
## 49.42857 3.133122 0.8909544
## 49.57143 3.097699 0.7907016
## 49.71429 2.914110 0.5411153
## 49.85714 2.836366 0.4011047
## 50.00000 2.692262 0.1950064
## 
## $青森県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%      95%
## 48.14286 11.838042 13.25513
## 48.28571  9.320655 10.95014
## 48.42857 11.378283 13.05123
## 48.57143 10.282956 12.08393
## 48.71429 11.302923 13.16471
## 48.85714 10.870681 12.82796
## 49.00000 11.411002 13.43479
## 49.14286 11.281652 13.38555
## 49.28571 11.597711 13.76835
## 49.42857 11.604234 13.84640
## 49.57143 11.813743 14.12074
## 49.71429 11.879498 14.25249
## 49.85714 12.037001 14.47226
## 50.00000 12.127118 14.62437
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 1.7786785 0.3221887 1.6514520 0.5021338 0.4308859 1.3794932 0.6288253
##  [8] 0.5953163 0.4562498 1.0784291 0.2521238 0.3965611 0.6251987 0.4645117
## 
## $岩手県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                  80%       95%
## 48.14286 -0.03887225 -1.001026
## 48.28571 -1.60864385 -2.630765
## 48.42857 -0.38637464 -1.465135
## 48.57143 -1.63734297 -2.769914
## 48.71429 -1.80562570 -2.989563
## 48.85714 -0.95001481 -2.183182
## 49.00000 -1.79010641 -3.070612
## 49.14286 -2.04584473 -3.443991
## 49.28571 -2.30288828 -3.763488
## 49.42857 -1.79384424 -3.314334
## 49.57143 -2.72899440 -4.307103
## 49.71429 -2.68956544 -4.323262
## 49.85714 -2.56247889 -4.249933
## 50.00000 -2.82158021 -4.561132
## 
## $岩手県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 3.596229 4.558383
## 48.28571 2.253021 3.275142
## 48.42857 3.689279 4.768039
## 48.57143 2.641611 3.774181
## 48.71429 2.667397 3.851335
## 48.85714 3.709001 4.942168
## 49.00000 3.047757 4.328262
## 49.14286 3.236477 4.634623
## 49.28571 3.215388 4.675987
## 49.42857 3.950702 5.471192
## 49.57143 3.233242 4.811351
## 49.71429 3.482688 5.116384
## 49.85714 3.812876 5.500331
## 50.00000 3.750604 5.490156
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 27.00927 26.89532 21.27887 24.12905 22.21499 24.06720 22.25237 22.49432
##  [9] 20.78976 20.77561 20.00237 20.45906 20.45407 21.02711
## 
## $宮城県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%       95%
## 48.14286 21.59373 18.726918
## 48.28571 21.39896 18.489367
## 48.42857 15.62923 12.638495
## 48.57143 18.47394 15.480303
## 48.71429 16.33814 13.227118
## 48.85714 18.02497 14.826404
## 49.00000 15.86723 12.487147
## 49.14286 15.94049 12.471097
## 49.28571 14.09215 10.546659
## 49.42857 14.03980 10.474071
## 49.57143 13.24003  9.660262
## 49.71429 13.68808 10.103730
## 49.85714 13.66438 10.070132
## 50.00000 14.21439 10.607959
## 
## $宮城県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 32.42482 35.29163
## 48.28571 32.39168 35.30127
## 48.42857 26.92850 29.91924
## 48.57143 29.78416 32.77780
## 48.71429 28.09184 31.20285
## 48.85714 30.10943 33.30799
## 49.00000 28.63751 32.01760
## 49.14286 29.04815 32.51754
## 49.28571 27.48736 31.03285
## 49.42857 27.51143 31.07716
## 49.57143 26.76471 30.34448
## 49.71429 27.23005 30.81440
## 49.85714 27.24376 30.83800
## 50.00000 27.83982 31.44626
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 0.3602679 0.4065124 0.4527710 0.4638847 0.4704887 0.4725803 0.4735879
##  [8] 0.4739514 0.4741108 0.4741719 0.4741976 0.4742078 0.4742120 0.4742136
## 
## $秋田県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                 80%       95%
## 48.14286 -0.9835154 -1.694871
## 48.28571 -0.9511201 -1.669807
## 48.42857 -0.9189598 -1.645110
## 48.57143 -0.9095187 -1.636555
## 48.71429 -0.9038831 -1.631432
## 48.85714 -0.9022406 -1.630027
## 49.00000 -0.9015873 -1.629561
## 49.14286 -0.9015269 -1.629661
## 49.28571 -0.9016551 -1.629942
## 49.42857 -0.9018743 -1.630309
## 49.57143 -0.9021263 -1.630708
## 49.71429 -0.9023926 -1.631121
## 49.85714 -0.9026645 -1.631539
## 50.00000 -0.9029385 -1.631959
## 
## $秋田県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 1.704051 2.415407
## 48.28571 1.764145 2.482832
## 48.42857 1.824502 2.550652
## 48.57143 1.837288 2.564324
## 48.71429 1.844861 2.572409
## 48.85714 1.847401 2.575187
## 49.00000 1.848763 2.576737
## 49.14286 1.849430 2.577564
## 49.28571 1.849877 2.578163
## 49.42857 1.850218 2.578653
## 49.57143 1.850522 2.579103
## 49.71429 1.850808 2.579536
## 49.85714 1.851088 2.579963
## 50.00000 1.851366 2.580386
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 8.754307 7.758086 8.304847 9.941706 8.065886 9.039518 9.206701 8.825772
##  [9] 8.825772 8.825772 8.825772 8.825772 8.825772 8.825772
## 
## $山形県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 7.265981 6.478109
## 48.28571 6.208031 5.387482
## 48.42857 6.695430 5.843456
## 48.57143 8.275039 7.392759
## 48.71429 6.343872 5.432292
## 48.85714 7.263881 6.323915
## 49.00000 7.379013 6.411493
## 49.14286 6.855973 5.813224
## 49.28571 6.788283 5.709702
## 49.42857 6.722771 5.609509
## 49.57143 6.659238 5.512345
## 49.71429 6.597517 5.417950
## 49.85714 6.537459 5.326100
## 50.00000 6.478938 5.236600
## 
## $山形県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%      95%
## 48.14286 10.242632 11.03050
## 48.28571  9.308140 10.12869
## 48.42857  9.914264 10.76624
## 48.57143 11.608373 12.49065
## 48.71429  9.787901 10.69948
## 48.85714 10.815156 11.75512
## 49.00000 11.034389 12.00191
## 49.14286 10.795571 11.83832
## 49.28571 10.863261 11.94184
## 49.42857 10.928774 12.04204
## 49.57143 10.992306 12.13920
## 49.71429 11.054028 12.23359
## 49.85714 11.114085 12.32544
## 50.00000 11.172606 12.41494
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 5.812420 6.458539 5.489237 5.953232 5.126954 4.894373 5.294463 5.014767
##  [9] 6.252719 5.151403 6.069021 5.381456 5.414571 5.391227
## 
## $福島県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%       95%
## 48.14286 3.150558 1.7414532
## 48.28571 3.745646 2.3095275
## 48.42857 2.584190 1.0463515
## 48.57143 3.018183 1.4644619
## 48.71429 2.152451 0.5778434
## 48.85714 1.905939 0.3239575
## 49.00000 2.293304 0.7045858
## 49.14286 1.973192 0.3630803
## 49.28571 3.200887 1.5853447
## 49.42857 2.086718 0.4643718
## 49.57143 2.997511 1.3715519
## 49.71429 2.303330 0.6738685
## 49.85714 2.331413 0.6992873
## 50.00000 2.303346 0.6687206
## 
## $福島県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%       95%
## 48.14286 8.474282  9.883387
## 48.28571 9.171431 10.607550
## 48.42857 8.394284  9.932123
## 48.57143 8.888281 10.442002
## 48.71429 8.101458  9.676065
## 48.85714 7.882806  9.464788
## 49.00000 8.295622  9.884340
## 49.14286 8.056341  9.666453
## 49.28571 9.304552 10.920094
## 49.42857 8.216088  9.838435
## 49.57143 9.140531 10.766491
## 49.71429 8.459582 10.089043
## 49.85714 8.497730 10.129855
## 50.00000 8.479108 10.113733
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 30.27716 38.52688 27.66735 21.06077 18.44287 28.69472 22.62757 23.77700
##  [9] 36.74032 19.12440 20.03822 17.72358 24.04699 17.74534
## 
## $茨城県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%        95%
## 48.14286 22.830194 18.8880081
## 48.28571 30.819952 26.7401496
## 48.42857 19.708944 15.4960169
## 48.57143 12.858594  8.5166225
## 48.71429 10.003958  5.5366684
## 48.85714 20.025544 15.4363574
## 49.00000 13.734078  9.0261482
## 49.14286 14.153959  9.0598276
## 49.28571 26.813880 21.5591421
## 49.42857  8.903573  3.4929926
## 49.57143  9.531242  3.9691850
## 49.71429  6.938045  1.2285282
## 49.85714 12.989909  7.1366460
## 50.00000  6.423231  0.4296684
## 
## $茨城県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 37.72413 41.66631
## 48.28571 46.23381 50.31362
## 48.42857 35.62576 39.83869
## 48.57143 29.26295 33.60492
## 48.71429 26.88178 31.34907
## 48.85714 37.36391 41.95309
## 49.00000 31.52106 36.22899
## 49.14286 33.40004 38.49418
## 49.28571 46.66675 51.92149
## 49.42857 29.34523 34.75581
## 49.57143 30.54520 36.10725
## 49.71429 28.50911 34.21863
## 49.85714 35.10406 40.95733
## 50.00000 29.06745 35.06101
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 16.42539 16.81563 15.77853 16.55352 16.71824 16.02130 16.32822 16.35613
##  [9] 16.35613 16.35613 16.35613 16.35613 16.35613 16.35613
## 
## $栃木県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%       95%
## 48.14286 12.92246 11.068118
## 48.28571 13.21161 11.303758
## 48.42857 12.07619 10.116284
## 48.57143 12.75539 10.744785
## 48.71429 12.82668 10.766620
## 48.85714 12.03851  9.930154
## 49.00000 12.25624 10.100666
## 49.14286 11.98348  9.668739
## 49.28571 11.86425  9.486393
## 49.42857 11.74810  9.308763
## 49.57143 11.63482  9.135502
## 49.71429 11.52418  8.966301
## 49.85714 11.41602  8.800890
## 50.00000 11.31018  8.639022
## 
## $栃木県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 19.92832 21.78266
## 48.28571 20.41964 22.32750
## 48.42857 19.48088 21.44078
## 48.57143 20.35165 22.36226
## 48.71429 20.60979 22.66986
## 48.85714 20.00409 22.11245
## 49.00000 20.40020 22.55578
## 49.14286 20.72878 23.04352
## 49.28571 20.84801 23.22587
## 49.42857 20.96416 23.40350
## 49.57143 21.07745 23.57676
## 49.71429 21.18808 23.74596
## 49.85714 21.29624 23.91137
## 50.00000 21.40208 24.07324
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 37.93783 27.60423 26.87744 28.25703 27.18026 32.55262 38.52385 35.92897
##  [9] 28.24173 26.26203 29.70257 32.59239 33.44443 33.68330
## 
## $群馬県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 32.75081 30.00497
## 48.28571 21.43105 18.16316
## 48.42857 20.46009 17.06295
## 48.57143 21.77320 18.34087
## 48.71429 20.40402 16.81689
## 48.85714 25.63074 21.96652
## 49.00000 31.36000 27.56769
## 49.14286 28.24120 24.17153
## 49.28571 19.97241 15.59490
## 49.42857 17.79733 13.31639
## 49.57143 21.15370 16.62821
## 49.71429 23.88930 19.28216
## 49.85714 24.46413 19.71024
## 50.00000 24.41751 19.51249
## 
## $群馬県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 43.12486 45.87070
## 48.28571 33.77742 37.04531
## 48.42857 33.29479 36.69193
## 48.57143 34.74085 38.17318
## 48.71429 33.95650 37.54362
## 48.85714 39.47450 43.13872
## 49.00000 45.68770 49.48002
## 49.14286 43.61675 47.68641
## 49.28571 36.51105 40.88856
## 49.42857 34.72673 39.20767
## 49.57143 38.25143 42.77693
## 49.71429 41.29549 45.90263
## 49.85714 42.42473 47.17862
## 50.00000 42.94910 47.85412
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 159.2210 164.8652 161.7496 163.9929 136.1383 174.4620 160.9542 161.4107
##  [9] 168.1341 168.7168 166.3287 159.3030 171.9711 163.9577
## 
## $埼玉県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 141.6784 132.3919
## 48.28571 146.7814 137.2084
## 48.42857 143.1403 133.2891
## 48.57143 144.8725 134.7508
## 48.71429 116.5202 106.1350
## 48.85714 154.3585 143.7163
## 49.00000 140.3768 129.4837
## 49.14286 137.7727 125.2595
## 49.28571 143.6956 130.7586
## 49.42857 143.5031 130.1558
## 49.57143 140.3630 126.6177
## 49.71429 132.6065 118.4743
## 49.85714 144.5633 130.0545
## 50.00000 135.8565 120.9807
## 
## $埼玉県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 176.7637 186.0502
## 48.28571 182.9490 192.5220
## 48.42857 180.3589 190.2100
## 48.57143 183.1132 193.2349
## 48.71429 155.7564 166.1415
## 48.85714 194.5655 205.2076
## 49.00000 181.5317 192.4247
## 49.14286 185.0487 197.5619
## 49.28571 192.5727 205.5096
## 49.42857 193.9304 207.2777
## 49.57143 192.2943 206.0397
## 49.71429 185.9994 200.1317
## 49.85714 199.3789 213.8877
## 50.00000 192.0588 206.9346
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 81.69472 81.06997 80.36223 78.63726 73.17932 81.53780 82.99395 80.30385
##  [9] 77.52220 77.03677 79.59312 76.12891 81.01170 82.43531
## 
## $千葉県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 69.05333 62.36138
## 48.28571 66.94101 59.46159
## 48.42857 65.76516 58.03794
## 48.57143 63.58663 55.61930
## 48.71429 57.68840 49.48800
## 48.85714 65.61877 57.19174
## 49.00000 66.65802 58.01030
## 49.14286 62.67853 53.34825
## 49.28571 59.16275 49.44385
## 49.42857 58.12843 48.11896
## 49.57143 60.15137 49.85953
## 49.71429 56.16801 45.60134
## 49.85714 60.54480 49.71028
## 50.00000 61.47463 50.37872
## 
## $千葉県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%       95%
## 48.14286  94.33611 101.02806
## 48.28571  95.19893 102.67835
## 48.42857  94.95931 102.68653
## 48.57143  93.68789 101.65522
## 48.71429  88.67024  96.87064
## 48.85714  97.45684 105.88386
## 49.00000  99.32988 107.97760
## 49.14286  97.92917 107.25944
## 49.28571  95.88165 105.60056
## 49.42857  95.94512 105.95459
## 49.57143  99.03487 109.32671
## 49.71429  96.08982 106.65649
## 49.85714 101.47860 112.31313
## 50.00000 103.39599 114.49191
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 583.9460 548.2110 598.6528 414.2436 352.3701 391.4332 555.3266 591.8759
##  [9] 563.7439 616.2799 432.1869 370.0629 408.7015 572.1240
## 
## $東京都$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 531.3247 503.4687
## 48.28571 487.9915 456.1132
## 48.42857 534.7606 500.9381
## 48.57143 347.7457 312.5439
## 48.71429 283.6957 247.3417
## 48.85714 320.8244 283.4464
## 49.00000 482.9584 444.6490
## 49.14286 510.7328 467.7783
## 49.28571 478.2926 433.0575
## 49.42857 527.7504 480.8857
## 49.57143 341.0746 292.8427
## 49.71429 276.6477 227.1968
## 49.85714 313.1861 262.6233
## 50.00000 474.6758 423.0899
## 
## $東京都$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 636.5673 664.4233
## 48.28571 608.4305 640.3087
## 48.42857 662.5450 696.3674
## 48.57143 480.7414 515.9432
## 48.71429 421.0445 457.3985
## 48.85714 462.0419 499.4199
## 49.00000 627.6948 666.0042
## 49.14286 673.0190 715.9735
## 49.28571 649.1952 694.4304
## 49.42857 704.8094 751.6741
## 49.57143 523.2991 571.5311
## 49.71429 463.4780 512.9290
## 49.85714 504.2168 554.7796
## 50.00000 669.5721 721.1580
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 233.92334 203.17448 189.47837 151.72965  93.51579 151.44460 213.28941
##  [8] 222.79652 208.35609 202.59757 161.20231 100.35168 156.39330 216.90599
## 
## $神奈川県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%       95%
## 48.14286 212.32492 200.89140
## 48.28571 178.89827 166.04722
## 48.42857 164.47397 151.23744
## 48.57143 126.70038 113.45068
## 48.71429  68.24484  54.86722
## 48.85714 125.67140 112.02789
## 49.00000 186.81331 172.79771
## 49.14286 193.64637 178.21521
## 49.28571 177.61448 161.34085
## 49.42857 170.69962 153.81386
## 49.57143 128.50959 111.20310
## 49.71429  66.79129  49.02550
## 49.85714 121.95475 103.72408
## 50.00000 181.62099 162.94224
## 
## $神奈川県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 255.5218 266.9553
## 48.28571 227.4507 240.3017
## 48.42857 214.4828 227.7193
## 48.57143 176.7589 190.0086
## 48.71429 118.7867 132.1644
## 48.85714 177.2178 190.8613
## 49.00000 239.7655 253.7811
## 49.14286 251.9467 267.3778
## 49.28571 239.0977 255.3713
## 49.42857 234.4955 251.3813
## 49.57143 193.8950 211.2015
## 49.71429 133.9121 151.6779
## 49.85714 190.8319 209.0625
## 50.00000 252.1910 270.8697
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 3.207545 4.540196 3.528098 4.605457 3.498238 4.525621 3.572318 4.456882
##  [9] 3.636099 4.397699 3.691015 4.346744 3.738297 4.302871
## 
## $新潟県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                 80%        95%
## 48.14286 0.20435031 -1.3854451
## 48.28571 1.40603104 -0.2530954
## 48.42857 0.39125209 -1.2692936
## 48.57143 1.40342319 -0.2916312
## 48.71429 0.21594010 -1.5216033
## 48.85714 1.22043731 -0.5292210
## 49.00000 0.19435812 -1.5938256
## 49.14286 1.05406359 -0.7472798
## 49.28571 0.16664301 -1.6699763
## 49.42857 0.90172744 -0.9489283
## 49.57143 0.13348929 -1.7497510
## 49.71429 0.76133489 -1.1366658
## 49.85714 0.09558398 -1.8327516
## 50.00000 0.63116568 -1.3125179
## 
## $新潟県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 6.210740 7.800536
## 48.28571 7.674360 9.333486
## 48.42857 6.664943 8.325489
## 48.57143 7.807491 9.502545
## 48.71429 6.780535 8.518079
## 48.85714 7.830804 9.580462
## 49.00000 6.950277 8.738461
## 49.14286 7.859701 9.661044
## 49.28571 7.105556 8.942175
## 49.42857 7.893671 9.744327
## 49.57143 7.248541 9.131781
## 49.71429 7.932152 9.830153
## 49.85714 7.381009 9.309345
## 50.00000 7.974577 9.918261
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 0.07075549 0.06806903 0.06548457 0.06299824 0.06060631 0.05830520
##  [7] 0.05609145 0.05396176 0.05191293 0.04994189 0.04804569 0.04622148
## [13] 0.04446653 0.04277822
## 
## $富山県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%       95%
## 48.14286 -2.340239 -3.616542
## 48.28571 -2.499303 -3.858387
## 48.42857 -2.638569 -4.070008
## 48.57143 -2.761668 -4.256956
## 48.71429 -2.871270 -4.423311
## 48.85714 -2.969414 -4.572191
## 49.00000 -3.057703 -4.706046
## 49.14286 -3.137428 -4.826847
## 49.28571 -3.209647 -4.936212
## 49.42857 -3.275242 -5.035488
## 49.57143 -3.334956 -5.125809
## 49.71429 -3.389425 -5.208146
## 49.85714 -3.439193 -5.283331
## 50.00000 -3.484736 -5.352090
## 
## $富山県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 2.481750 3.758053
## 48.28571 2.635441 3.994525
## 48.42857 2.769538 4.200977
## 48.57143 2.887664 4.382952
## 48.71429 2.992482 4.544524
## 48.85714 3.086024 4.688801
## 49.00000 3.169886 4.818229
## 49.14286 3.245351 4.934771
## 49.28571 3.313473 5.040038
## 49.42857 3.375126 5.135372
## 49.57143 3.431048 5.221901
## 49.71429 3.481868 5.300589
## 49.85714 3.528126 5.372264
## 50.00000 3.570293 5.437646
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 4.082155 4.082155 4.082155 4.082155 4.082155 4.082155 4.082155 4.082155
##  [9] 4.082155 4.082155 4.082155 4.082155 4.082155 4.082155
## 
## $石川県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                  80%       95%
## 48.14286  0.49277005 -1.407336
## 48.28571  0.29347415 -1.712132
## 48.42857  0.10415037 -2.001678
## 48.57143 -0.07656344 -2.278056
## 48.71429 -0.24974497 -2.542914
## 48.85714 -0.41626424 -2.797584
## 49.00000 -0.57683565 -3.043156
## 49.14286 -0.73205440 -3.280543
## 49.28571 -0.88242257 -3.510511
## 49.42857 -1.02836833 -3.733716
## 49.57143 -1.17026035 -3.950721
## 49.71429 -1.30841875 -4.162016
## 49.85714 -1.44312360 -4.368029
## 50.00000 -1.57462163 -4.569138
## 
## $石川県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%       95%
## 48.14286 7.671540  9.571646
## 48.28571 7.870836  9.876443
## 48.42857 8.060160 10.165988
## 48.57143 8.240874 10.442366
## 48.71429 8.414055 10.707225
## 48.85714 8.580575 10.961894
## 49.00000 8.741146 11.207467
## 49.14286 8.896365 11.444853
## 49.28571 9.046733 11.674822
## 49.42857 9.192679 11.898026
## 49.57143 9.334571 12.115031
## 49.71429 9.472729 12.326326
## 49.85714 9.607434 12.532340
## 50.00000 9.738932 12.733449
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 2.828993 2.908959 2.168076 2.251543 2.057921 1.750264 1.795608 1.599786
##  [9] 1.497821 1.483090 1.358260 1.327509 1.289594 1.225660
## 
## $福井県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                 80%        95%
## 48.14286  0.8571661 -0.1866560
## 48.28571  0.7671037 -0.3667258
## 48.42857 -0.1641634 -1.3987762
## 48.57143 -0.3057780 -1.6595422
## 48.71429 -0.5445018 -1.9221415
## 48.85714 -0.9379986 -2.3610788
## 49.00000 -0.9450250 -2.3958286
## 49.14286 -1.1611089 -2.6226383
## 49.28571 -1.2959651 -2.7749062
## 49.42857 -1.3247263 -2.8110945
## 49.57143 -1.4595454 -2.9512017
## 49.71429 -1.5012926 -2.9987699
## 49.85714 -1.5437643 -3.0436538
## 50.00000 -1.6122096 -3.1144869
## 
## $福井県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 4.800820 5.844642
## 48.28571 5.050813 6.184643
## 48.42857 4.500315 5.734928
## 48.57143 4.808865 6.162629
## 48.71429 4.660345 6.037984
## 48.85714 4.438526 5.861606
## 49.00000 4.536242 5.987045
## 49.14286 4.360680 5.822210
## 49.28571 4.291607 5.770548
## 49.42857 4.290906 5.777274
## 49.57143 4.176066 5.667722
## 49.71429 4.156311 5.653789
## 49.85714 4.122953 5.622842
## 50.00000 4.063529 5.565807
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1]  3.513674  7.894643  8.153254 11.995330 13.914901 15.115093 13.606224
##  [8] 13.047687  9.978637  8.876495  6.278083  5.619857  5.650599  7.351677
## 
## $山梨県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%           95%
## 48.14286  1.286216  1.070710e-01
## 48.28571  5.440831  4.141861e+00
## 48.42857  5.362739  3.885530e+00
## 48.57143  9.080143  7.536936e+00
## 48.71429 10.904040  9.310186e+00
## 48.85714 12.050799  1.042866e+01
## 49.00000 10.488162  8.837559e+00
## 49.14286  9.914506  8.255900e+00
## 49.28571  6.776448  5.081312e+00
## 49.42857  5.573360  3.824787e+00
## 49.57143  2.806471  9.687108e-01
## 49.71429  1.945208 -3.393988e-05
## 49.85714  1.762510 -2.957205e-01
## 50.00000  3.277484  1.120737e+00
## 
## $山梨県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%       95%
## 48.14286  5.741132  6.920277
## 48.28571 10.348455 11.647425
## 48.42857 10.943769 12.420978
## 48.57143 14.910517 16.453724
## 48.71429 16.925762 18.519615
## 48.85714 18.179387 19.801526
## 49.00000 16.724286 18.374889
## 49.14286 16.180867 17.839473
## 49.28571 13.180825 14.875961
## 49.42857 12.179629 13.928203
## 49.57143  9.749694 11.587454
## 49.71429  9.294506 11.239748
## 49.85714  9.538688 11.596918
## 50.00000 11.425870 13.582617
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 19.57789 19.57789 19.57789 19.57789 19.57789 19.57789 19.57789 19.57789
##  [9] 19.57789 19.57789 19.57789 19.57789 19.57789 19.57789
## 
## $長野県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 16.07665 14.22320
## 48.28571 15.81438 13.82210
## 48.42857 15.56924 13.44719
## 48.57143 15.33825 13.09392
## 48.71429 15.11921 12.75892
## 48.85714 14.91044 12.43963
## 49.00000 14.71061 12.13403
## 49.14286 14.51867 11.84048
## 49.28571 14.33375 11.55767
## 49.42857 14.15513 11.28450
## 49.57143 13.98222 11.02005
## 49.71429 13.81448 10.76352
## 49.85714 13.65150 10.51425
## 50.00000 13.49287 10.27166
## 
## $長野県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 23.07913 24.93258
## 48.28571 23.34140 25.33368
## 48.42857 23.58654 25.70859
## 48.57143 23.81753 26.06186
## 48.71429 24.03657 26.39686
## 48.85714 24.24535 26.71615
## 49.00000 24.44517 27.02176
## 49.14286 24.63711 27.31530
## 49.28571 24.82203 27.59811
## 49.42857 25.00065 27.87128
## 49.57143 25.17357 28.13574
## 49.71429 25.34130 28.39226
## 49.85714 25.50429 28.64153
## 50.00000 25.66291 28.88412
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 42.96321 35.44229 29.89501 25.65467 29.31438 35.55195 37.11626 36.44944
##  [9] 33.61169 31.23720 31.28194 32.82734 34.20537 34.74373
## 
## $岐阜県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 38.68538 36.42083
## 48.28571 30.81223 28.36122
## 48.42857 24.78980 22.08727
## 48.57143 20.47115 17.72717
## 48.71429 24.08111 21.31079
## 48.85714 30.24218 27.43136
## 49.00000 31.49630 28.52127
## 49.14286 30.26679 26.99390
## 49.28571 27.03340 23.55107
## 49.42857 24.40155 20.78298
## 49.57143 24.29633 20.59837
## 49.71429 25.71226 21.94576
## 49.85714 26.91212 23.05131
## 50.00000 27.20894 23.22027
## 
## $岐阜県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 47.24104 49.50558
## 48.28571 40.07236 42.52337
## 48.42857 35.00022 37.70275
## 48.57143 30.83818 33.58217
## 48.71429 34.54765 37.31797
## 48.85714 40.86171 43.67254
## 49.00000 42.73623 45.71126
## 49.14286 42.63208 45.90497
## 49.28571 40.18998 43.67232
## 49.42857 38.07284 41.69142
## 49.57143 38.26755 41.96551
## 49.71429 39.94241 43.70891
## 49.85714 41.49863 45.35944
## 50.00000 42.27852 46.26719
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 41.96648 34.80545 36.75021 34.95844 30.45418 25.68777 36.01134 37.98685
##  [9] 32.47707 33.83553 32.50176 29.19077 25.69107 33.26772
## 
## $静岡県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%       95%
## 48.14286 34.50360 30.552987
## 48.28571 26.22018 21.675409
## 48.42857 27.52697 22.644480
## 48.57143 25.21740 20.060806
## 48.71429 20.24102 14.834503
## 48.85714 15.02839  9.385657
## 49.00000 24.92498 19.056210
## 49.14286 25.82927 19.393439
## 49.28571 19.63035 12.829714
## 49.42857 20.40305 13.292326
## 49.57143 18.52520 11.126460
## 49.71429 14.69499  7.021385
## 49.85714 10.69520  2.756868
## 50.00000 17.78821  9.593858
## 
## $静岡県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 49.42937 53.37998
## 48.28571 43.39072 47.93549
## 48.42857 45.97345 50.85594
## 48.57143 44.69947 49.85607
## 48.71429 40.66733 46.07385
## 48.85714 36.34716 41.98989
## 49.00000 47.09770 52.96647
## 49.14286 50.14443 56.58026
## 49.28571 45.32379 52.12443
## 49.42857 47.26802 54.37874
## 49.57143 46.47832 53.87706
## 49.71429 43.68655 51.36015
## 49.85714 40.68694 48.62527
## 50.00000 48.74722 56.94158
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 225.3428 241.0771 241.4363 202.5017 160.6504 223.9671 255.2338 242.1928
##  [9] 254.9018 255.1919 223.7435 189.9392 241.0816 266.3365
## 
## $愛知県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 206.7084 196.8439
## 48.28571 218.9340 207.2122
## 48.42857 216.2692 202.9465
## 48.57143 174.6367 159.8859
## 48.71429 130.3267 114.2743
## 48.85714 191.3695 174.1134
## 49.00000 220.5110 202.1298
## 49.14286 200.7960 178.8818
## 49.28571 209.4561 185.3986
## 49.42857 206.0298 180.0049
## 49.57143 171.1267 143.2730
## 49.71429 134.0810 104.5114
## 49.85714 182.1600 150.9688
## 50.00000 204.5031 171.7705
## 
## $愛知県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 243.9772 253.8416
## 48.28571 263.2201 274.9419
## 48.42857 266.6035 279.9261
## 48.57143 230.3667 245.1176
## 48.71429 190.9742 207.0266
## 48.85714 256.5647 273.8208
## 49.00000 289.9566 308.3378
## 49.14286 283.5897 305.5038
## 49.28571 300.3475 324.4050
## 49.42857 304.3541 330.3790
## 49.57143 276.3604 304.2141
## 49.71429 245.7975 275.3670
## 49.85714 300.0032 331.1944
## 50.00000 328.1699 360.9025
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 18.47068 18.31378 13.81313 12.05622 11.66139 14.24219 15.57280 14.94624
##  [9] 15.31391 14.37793 14.42098 14.90287 14.17582 14.40498
## 
## $三重県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%       95%
## 48.14286 14.321463 12.125000
## 48.28571 13.678644 11.224949
## 48.42857  8.749741  6.069345
## 48.57143  6.811457  4.035044
## 48.71429  6.371873  3.571772
## 48.85714  8.806433  5.928916
## 49.00000 10.069022  7.155499
## 49.14286  9.049474  5.927912
## 49.28571  9.116852  5.836330
## 49.42857  7.998402  4.621284
## 49.57143  7.949420  4.523584
## 49.71429  8.294785  4.796678
## 49.85714  7.466486  3.914778
## 50.00000  7.628239  4.040851
## 
## $三重県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 22.61990 24.81636
## 48.28571 22.94892 25.40262
## 48.42857 18.87652 21.55691
## 48.57143 17.30099 20.07741
## 48.71429 16.95091 19.75101
## 48.85714 19.67795 22.55547
## 49.00000 21.07658 23.99010
## 49.14286 20.84302 23.96458
## 49.28571 21.51096 24.79148
## 49.42857 20.75746 24.13458
## 49.57143 20.89254 24.31838
## 49.71429 21.51095 25.00905
## 49.85714 20.88516 24.43686
## 50.00000 21.18171 24.76910
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 6.844700 8.036488 7.041375 7.394132 7.079315 7.182869 7.082931 7.113010
##  [9] 7.081167 7.089785 7.079598 7.082022 7.078749 7.079413
## 
## $滋賀県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%         95%
## 48.14286 2.919625  0.84181571
## 48.28571 4.047135  1.93529950
## 48.42857 2.632167  0.29807389
## 48.57143 2.901299  0.52293709
## 48.71429 2.427513 -0.03500272
## 48.85714 2.447839 -0.05873382
## 49.00000 2.246766 -0.31334452
## 49.14286 2.197174 -0.40511187
## 49.28571 2.081769 -0.56475319
## 49.42857 2.013956 -0.67302503
## 49.57143 1.927077 -0.80050209
## 49.71429 1.855703 -0.91094362
## 49.85714 1.779285 -1.02608154
## 50.00000 1.708353 -1.13491513
## 
## $滋賀県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 10.76978 12.84758
## 48.28571 12.02584 14.13768
## 48.42857 11.45058 13.78468
## 48.57143 11.88697 14.26533
## 48.71429 11.73112 14.19363
## 48.85714 11.91790 14.42447
## 49.00000 11.91910 14.47921
## 49.14286 12.02885 14.63113
## 49.28571 12.08057 14.72709
## 49.42857 12.16561 14.85260
## 49.57143 12.23212 14.95970
## 49.71429 12.30834 15.07499
## 49.85714 12.37821 15.18358
## 50.00000 12.45047 15.29374
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 55.94328 50.44194 51.12081 49.82522 47.21474 52.57376 53.43684 51.66948
##  [9] 52.54159 53.55509 53.77410 51.81306 56.52009 57.78314
## 
## $京都府$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 48.10706 43.95881
## 48.28571 41.44302 36.67927
## 48.42857 41.81883 36.89466
## 48.57143 40.22976 35.15022
## 48.71429 37.33450 32.10421
## 48.85714 42.41673 37.03992
## 49.00000 43.01036 37.49092
## 49.14286 40.75444 34.97636
## 49.28571 41.26098 35.28939
## 49.42857 41.97474 35.84448
## 49.57143 41.90157 35.61664
## 49.71429 39.65538 33.21950
## 49.85714 44.08379 37.50041
## 50.00000 45.07433 38.34669
## 
## $京都府$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 63.77951 67.92776
## 48.28571 59.44086 64.20461
## 48.42857 60.42279 65.34696
## 48.57143 59.42069 64.50022
## 48.71429 57.09498 62.32526
## 48.85714 62.73079 68.10760
## 49.00000 63.86333 69.38277
## 49.14286 62.58453 68.36261
## 49.28571 63.82219 69.79379
## 49.42857 65.13544 71.26571
## 49.57143 65.64662 71.93156
## 49.71429 63.97074 70.40663
## 49.85714 68.95639 75.53976
## 50.00000 70.49195 77.21958
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 386.3996 383.0857 400.6735 348.2842 276.3308 300.0083 403.7226 379.2294
##  [9] 382.5219 396.6659 354.5349 296.6707 315.7119 399.1179
## 
## $大阪府$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 351.7854 333.4618
## 48.28571 343.2614 322.1797
## 48.42857 359.2405 337.3072
## 48.57143 305.3026 282.5495
## 48.71429 231.8546 208.3103
## 48.85714 254.0860 229.7762
## 49.00000 356.3983 331.3464
## 49.14286 324.2050 295.0768
## 49.28571 323.6618 292.5031
## 49.42857 335.4194 302.9976
## 49.57143 290.9917 257.3540
## 49.71429 230.9109 196.0998
## 49.85714 247.8078 211.8616
## 50.00000 329.1352 292.0886
## 
## $大阪府$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 421.0138 439.3375
## 48.28571 422.9100 443.9917
## 48.42857 442.1065 464.0399
## 48.57143 391.2658 414.0188
## 48.71429 320.8071 344.3514
## 48.85714 345.9306 370.2404
## 49.00000 451.0468 476.0987
## 49.14286 434.2537 463.3819
## 49.28571 441.3820 472.5407
## 49.42857 457.9123 490.3342
## 49.57143 418.0781 451.7158
## 49.71429 362.4305 397.2416
## 49.85714 383.6159 419.5622
## 50.00000 469.1005 506.1471
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 186.8015 152.0610 171.1604 149.5022 129.6516 161.0137 168.7098 196.2689
##  [9] 167.4688 183.3024 165.3475 148.8913 174.8906 181.2708
## 
## $兵庫県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 173.5866 166.5911
## 48.28571 138.0080 130.5687
## 48.42857 156.3165 148.4587
## 48.57143 133.9075 125.6521
## 48.71429 113.3406 104.7061
## 48.85714 144.0166 135.0188
## 49.00000 151.0532 141.7064
## 49.14286 175.3348 164.2530
## 49.28571 145.3571 133.6519
## 49.42857 160.0728 147.7758
## 49.57143 141.0514 128.1898
## 49.71429 123.5735 110.1711
## 49.85714 148.5909 134.6686
## 50.00000 154.0244 139.6010
## 
## $兵庫県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 200.0164 207.0120
## 48.28571 166.1140 173.5532
## 48.42857 186.0043 193.8622
## 48.57143 165.0969 173.3522
## 48.71429 145.9626 154.5971
## 48.85714 178.0108 187.0086
## 49.00000 186.3664 195.7133
## 49.14286 217.2030 228.2849
## 49.28571 189.5804 201.2856
## 49.42857 206.5319 218.8289
## 49.57143 189.6437 202.5052
## 49.71429 174.2091 187.6115
## 49.85714 201.1904 215.1127
## 50.00000 208.5172 222.9406
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 29.17264 28.39244 23.47314 22.59640 24.92715 26.30084 28.64954 28.91090
##  [9] 26.97221 25.67724 24.55040 25.96915 26.40371 27.98985
## 
## $奈良県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 24.74353 22.39890
## 48.28571 23.66975 21.16971
## 48.42857 18.29391 15.55220
## 48.57143 17.39954 14.64849
## 48.71429 19.71975 16.96311
## 48.85714 20.96654 18.14273
## 49.00000 23.12034 20.19336
## 49.14286 23.15829 20.11305
## 49.28571 21.04292 17.90414
## 49.42857 19.65142 16.46154
## 49.57143 18.44128 15.20731
## 49.71429 19.77191 16.49128
## 49.85714 20.08699 16.74312
## 50.00000 21.53436 18.11703
## 
## $奈良県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 33.60175 35.94638
## 48.28571 33.11513 35.61517
## 48.42857 28.65236 31.39408
## 48.57143 27.79325 30.54430
## 48.71429 30.13455 32.89119
## 48.85714 31.63514 34.45895
## 49.00000 34.17874 37.10572
## 49.14286 34.66350 37.70875
## 49.28571 32.90150 36.04028
## 49.42857 31.70307 34.89295
## 49.57143 30.65952 33.89349
## 49.71429 32.16640 35.44703
## 49.85714 32.72043 36.06430
## 50.00000 34.44534 37.86267
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 9.372607 7.995486 9.367917 8.098360 9.764631 8.529518 8.530665 8.735645
##  [9] 8.912014 9.176813 8.182956 9.311967 8.711001 8.194554
## 
## $和歌山県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 7.169042 6.002546
## 48.28571 5.647586 4.404683
## 48.42857 6.947960 5.666912
## 48.57143 5.552380 4.204620
## 48.71429 7.148014 5.762860
## 48.85714 5.800576 4.355961
## 49.00000 5.732469 4.251194
## 49.14286 5.888737 4.381675
## 49.28571 6.013619 4.479302
## 49.42857 6.198102 4.621268
## 49.57143 5.152748 3.548653
## 49.71429 6.207332 4.563838
## 49.85714 5.555084 3.884442
## 50.00000 4.969072 3.261605
## 
## $和歌山県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 11.57617 12.74267
## 48.28571 10.34339 11.58629
## 48.42857 11.78787 13.06892
## 48.57143 10.64434 11.99210
## 48.71429 12.38125 13.76640
## 48.85714 11.25846 12.70307
## 49.00000 11.32886 12.81014
## 49.14286 11.58255 13.08962
## 49.28571 11.81041 13.34473
## 49.42857 12.15552 13.73236
## 49.57143 11.21316 12.81726
## 49.71429 12.41660 14.06010
## 49.85714 11.86692 13.53756
## 50.00000 11.42004 13.12750
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 0.6195035 0.7687252 0.6457358 0.7004105 0.5790349 0.5129163 0.4834522
##  [8] 0.4972672 0.5828898 0.4072187 0.5787697 0.3973458 0.4751801 0.4661426
## 
## $鳥取県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                 80%        95%
## 48.14286 -0.3262946 -0.8269698
## 48.28571 -0.1929518 -0.7020328
## 48.42857 -0.3259516 -0.8403319
## 48.57143 -0.2778046 -0.7956404
## 48.71429 -0.4035924 -0.9237639
## 48.85714 -0.4728096 -0.9946214
## 49.00000 -0.5045388 -1.0275496
## 49.14286 -0.4908633 -1.0139480
## 49.28571 -0.4062350 -0.9298460
## 49.42857 -0.5827588 -1.1068212
## 49.57143 -0.4119611 -0.9364224
## 49.71429 -0.5940676 -1.1188902
## 49.85714 -0.5168649 -1.0420218
## 50.00000 -0.5264967 -1.0519682
## 
## $鳥取県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 1.565302 2.065977
## 48.28571 1.730402 2.239483
## 48.42857 1.617423 2.131804
## 48.57143 1.678626 2.196461
## 48.71429 1.561662 2.081834
## 48.85714 1.498642 2.020454
## 49.00000 1.471443 1.994454
## 49.14286 1.485398 2.008482
## 49.28571 1.572014 2.095625
## 49.42857 1.397196 1.921259
## 49.57143 1.569501 2.093962
## 49.71429 1.388759 1.913582
## 49.85714 1.467225 1.992382
## 50.00000 1.458782 1.984253
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 0.4787879 0.4787879 0.4787879 0.4787879 0.4787879 0.4787879 0.4787879
##  [8] 0.4787879 0.4787879 0.4787879 0.4787879 0.4787879 0.4787879 0.4787879
## 
## $島根県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%       95%
## 48.14286 -6.052182 -9.509469
## 48.28571 -6.052182 -9.509469
## 48.42857 -6.052182 -9.509469
## 48.57143 -6.052182 -9.509469
## 48.71429 -6.052182 -9.509469
## 48.85714 -6.052182 -9.509469
## 49.00000 -6.052182 -9.509469
## 49.14286 -6.052182 -9.509469
## 49.28571 -6.052182 -9.509469
## 49.42857 -6.052182 -9.509469
## 49.57143 -6.052182 -9.509469
## 49.71429 -6.052182 -9.509469
## 49.85714 -6.052182 -9.509469
## 50.00000 -6.052182 -9.509469
## 
## $島根県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 7.009758 10.46704
## 48.28571 7.009758 10.46704
## 48.42857 7.009758 10.46704
## 48.57143 7.009758 10.46704
## 48.71429 7.009758 10.46704
## 48.85714 7.009758 10.46704
## 49.00000 7.009758 10.46704
## 49.14286 7.009758 10.46704
## 49.28571 7.009758 10.46704
## 49.42857 7.009758 10.46704
## 49.57143 7.009758 10.46704
## 49.71429 7.009758 10.46704
## 49.85714 7.009758 10.46704
## 50.00000 7.009758 10.46704
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 10.738929  9.972217 10.771516 10.936307 10.618587 10.914804 10.810319
##  [8] 10.780713 10.773086 10.771121 10.770615 10.770484 10.770451 10.770442
## 
## $岡山県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 7.396584 5.627253
## 48.28571 6.363453 4.453089
## 48.42857 7.063912 5.101225
## 48.57143 7.159652 5.160412
## 48.71429 6.780313 4.748453
## 48.85714 7.017407 4.954250
## 49.00000 6.855068 4.761285
## 49.14286 6.687840 4.521204
## 49.28571 6.597379 4.386893
## 49.42857 6.525071 4.277348
## 49.57143 6.458004 4.175046
## 49.71429 6.392987 4.075679
## 49.85714 6.329181 3.978115
## 50.00000 6.266345 3.882021
## 
## $岡山県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 14.08127 15.85061
## 48.28571 13.58098 15.49135
## 48.42857 14.47912 16.44181
## 48.57143 14.71296 16.71220
## 48.71429 14.45686 16.48872
## 48.85714 14.81220 16.87536
## 49.00000 14.76557 16.85935
## 49.14286 14.87359 17.04022
## 49.28571 14.94879 17.15928
## 49.42857 15.01717 17.26489
## 49.57143 15.08323 17.36618
## 49.71429 15.14798 17.46529
## 49.85714 15.21172 17.56279
## 50.00000 15.27454 17.65886
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1]  65.65222  67.71059  78.01959  78.08698  80.20864  85.59511  88.32084
##  [8]  91.10011  94.98951  98.25036 101.37876 104.84856 108.18882 111.45356
## 
## $広島県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 60.67287 58.03696
## 48.28571 62.04063 59.03913
## 48.42857 71.91368 68.68140
## 48.57143 70.82612 66.98245
## 48.71429 72.07669 67.77189
## 48.85714 76.72428 72.02835
## 49.00000 78.51795 73.32862
## 49.14286 80.38397 74.71118
## 49.28571 83.39624 77.25914
## 49.42857 85.72032 79.08732
## 49.57143 87.89457 80.75647
## 49.71429 90.40894 82.76507
## 49.85714 92.76671 84.60274
## 50.00000 95.02905 86.33443
## 
## $広島県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%       95%
## 48.14286  70.63156  73.26747
## 48.28571  73.38056  76.38206
## 48.42857  84.12551  87.35778
## 48.57143  85.34784  89.19151
## 48.71429  88.34060  92.64540
## 48.85714  94.46593  99.16186
## 49.00000  98.12373 103.31306
## 49.14286 101.81626 107.48904
## 49.28571 106.58278 112.71988
## 49.42857 110.78041 117.41341
## 49.57143 114.86295 122.00104
## 49.71429 119.28817 126.93204
## 49.85714 123.61094 131.77491
## 50.00000 127.87808 136.57269
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 2.891337 3.079597 2.937559 2.689533 3.170264 3.629291 3.330442 3.246275
##  [9] 3.115460 3.075913 2.921454 3.314960 3.194541 3.212446
## 
## $山口県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                  80%       95%
## 48.14286  0.28196061 -1.099360
## 48.28571  0.26163079 -1.230110
## 48.42857 -0.07629815 -1.671738
## 48.57143 -0.41409342 -2.057054
## 48.71429 -0.03159174 -1.726552
## 48.85714  0.38444697 -1.333270
## 49.00000  0.03137365 -1.715048
## 49.14286 -0.18097468 -1.995251
## 49.28571 -0.38001541 -2.230408
## 49.42857 -0.45709431 -2.327355
## 49.57143 -0.65118564 -2.542427
## 49.71429 -0.27929226 -2.181975
## 49.85714 -0.42469062 -2.340596
## 50.00000 -0.42037941 -2.343481
## 
## $山口県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 5.500712 6.882033
## 48.28571 5.897562 7.389303
## 48.42857 5.951415 7.546855
## 48.57143 5.793159 7.436119
## 48.71429 6.372120 8.067080
## 48.85714 6.874135 8.591852
## 49.00000 6.629511 8.375933
## 49.14286 6.673525 8.487801
## 49.28571 6.610935 8.461328
## 49.42857 6.608920 8.479181
## 49.57143 6.494093 8.385334
## 49.71429 6.909213 8.811895
## 49.85714 6.813772 8.729677
## 50.00000 6.845271 8.768373
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 0.5132699 0.9390570 0.8776613 0.6698026 0.6207266 0.9562902 0.5660281
##  [8] 0.5518617 0.4321571 0.5497042 0.7018494 0.7223311 0.4546327 0.7366857
## 
## $徳島県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                 80%       95%
## 48.14286 -1.1707105 -2.062156
## 48.28571 -0.8267449 -1.761504
## 48.42857 -0.9057884 -1.849890
## 48.57143 -1.1311221 -2.084474
## 48.71429 -1.1975051 -2.160019
## 48.85714 -0.8790852 -1.850674
## 49.00000 -1.2863324 -2.266913
## 49.14286 -1.3911706 -2.419750
## 49.28571 -1.5432898 -2.589028
## 49.42857 -1.4484319 -2.506181
## 49.57143 -1.3187211 -2.388347
## 49.71429 -1.3204275 -2.401799
## 49.85714 -1.6100756 -2.703066
## 50.00000 -1.3497415 -2.454229
## 
## $徳島県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 2.197250 3.088696
## 48.28571 2.704859 3.639618
## 48.42857 2.661111 3.605212
## 48.57143 2.470727 3.424079
## 48.71429 2.438958 3.401472
## 48.85714 2.791666 3.763255
## 49.00000 2.418389 3.398969
## 49.14286 2.494894 3.523473
## 49.28571 2.407604 3.453342
## 49.42857 2.547840 3.605590
## 49.57143 2.722420 3.792045
## 49.71429 2.765090 3.846461
## 49.85714 2.519341 3.612332
## 50.00000 2.823113 3.927601
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 4.661381 5.227344 5.227344 5.227344 5.227344 5.227344 5.227344 5.227344
##  [9] 5.227344 5.227344 5.227344 5.227344 5.227344 5.227344
## 
## $香川県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 2.828971 1.858952
## 48.28571 3.301045 2.281323
## 48.42857 3.275707 2.242572
## 48.57143 3.250694 2.204318
## 48.71429 3.225993 2.166542
## 48.85714 3.201594 2.129227
## 49.00000 3.177485 2.092355
## 49.14286 3.153657 2.055913
## 49.28571 3.130099 2.019884
## 49.42857 3.106802 1.984256
## 49.57143 3.083759 1.949014
## 49.71429 3.060962 1.914148
## 49.85714 3.038401 1.879645
## 50.00000 3.016071 1.845493
## 
## $香川県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 6.493791 7.463810
## 48.28571 7.153643 8.173365
## 48.42857 7.178981 8.212115
## 48.57143 7.203994 8.250369
## 48.71429 7.228695 8.288146
## 48.85714 7.253094 8.325461
## 49.00000 7.277203 8.362332
## 49.14286 7.301031 8.398775
## 49.28571 7.324589 8.434804
## 49.42857 7.347885 8.470432
## 49.57143 7.370928 8.505673
## 49.71429 7.393726 8.540540
## 49.85714 7.416287 8.575043
## 50.00000 7.438617 8.609194
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 2.035495 1.840234 2.699992 2.399242 2.485160 2.596493 2.891436 2.762765
##  [9] 2.762765 2.762765 2.762765 2.762765 2.762765 2.762765
## 
## $愛媛県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                 80%       95%
## 48.14286 -0.2940435 -1.527226
## 48.28571 -0.9131557 -2.370712
## 48.42857 -0.4201947 -2.071922
## 48.57143 -1.0489417 -2.874300
## 48.71429 -1.2624230 -3.246274
## 48.85714 -1.4282781 -3.558863
## 49.00000 -1.3926269 -3.660473
## 49.14286 -1.6629515 -4.005785
## 49.28571 -1.8341464 -4.267605
## 49.42857 -1.9991907 -4.520018
## 49.57143 -2.1587032 -4.763971
## 49.71429 -2.3132055 -5.000262
## 49.85714 -2.4631420 -5.229570
## 50.00000 -2.6088950 -5.452480
## 
## $愛媛県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%       95%
## 48.14286 4.365033  5.598216
## 48.28571 4.593624  6.051180
## 48.42857 5.820178  7.471905
## 48.57143 5.847426  7.672784
## 48.71429 6.232743  8.216593
## 48.85714 6.621265  8.751850
## 49.00000 7.175499  9.443344
## 49.14286 7.188482  9.531315
## 49.28571 7.359677  9.793135
## 49.42857 7.524722 10.045549
## 49.57143 7.684234 10.289502
## 49.71429 7.838736 10.525793
## 49.85714 7.988673 10.755101
## 50.00000 8.134426 10.978011
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 17.10179 17.10179 17.10179 17.10179 17.10179 17.10179 17.10179 17.10179
##  [9] 17.10179 17.10179 17.10179 17.10179 17.10179 17.10179
## 
## $高知県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 15.03183 13.93606
## 48.28571 14.75394 13.51106
## 48.42857 14.50562 13.13129
## 48.57143 14.27906 12.78480
## 48.71429 14.06939 12.46413
## 48.85714 13.87330 12.16424
## 49.00000 13.68846 11.88155
## 49.14286 13.51312 11.61340
## 49.28571 13.34596 11.35775
## 49.42857 13.18594 11.11301
## 49.57143 13.03220 10.87788
## 49.71429 12.88406 10.65132
## 49.85714 12.74094 10.43245
## 50.00000 12.60238 10.22054
## 
## $高知県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 19.17175 20.26751
## 48.28571 19.44964 20.69252
## 48.42857 19.69796 21.07229
## 48.57143 19.92451 21.41877
## 48.71429 20.13419 21.73945
## 48.85714 20.33028 22.03934
## 49.00000 20.51512 22.32203
## 49.14286 20.69045 22.59018
## 49.28571 20.85761 22.84582
## 49.42857 21.01764 23.09057
## 49.57143 21.17138 23.32569
## 49.71429 21.31952 23.55225
## 49.85714 21.46263 23.77112
## 50.00000 21.60119 23.98303
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 51.67495 47.38357 49.56970 61.83357 72.10406 75.64191 67.62010 60.62232
##  [9] 59.95413 60.77163 64.52878 70.89097 73.81400 67.74423
## 
## $福岡県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 37.93417 30.66023
## 48.28571 31.18044 22.60302
## 48.42857 31.96302 22.64261
## 48.57143 43.52357 33.83085
## 48.71429 53.52784 43.69418
## 48.85714 56.36197 46.15579
## 49.00000 46.70119 35.62739
## 49.14286 36.75550 24.12116
## 49.28571 33.78225 19.92769
## 49.42857 32.92376 18.18198
## 49.57143 35.44478 20.04864
## 49.71429 40.75918 24.80838
## 49.85714 42.54097 25.98603
## 50.00000 35.16850 17.92395
## 
## $福岡県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%       95%
## 48.14286  65.41574  72.68967
## 48.28571  63.58670  72.16412
## 48.42857  67.17639  76.49680
## 48.57143  80.14356  89.83628
## 48.71429  90.68028 100.51394
## 48.85714  94.92184 105.12802
## 49.00000  88.53900  99.61280
## 49.14286  84.48915  97.12349
## 49.28571  86.12601  99.98056
## 49.42857  88.61951 103.36128
## 49.57143  93.61278 109.00892
## 49.71429 101.02276 116.97356
## 49.85714 105.08703 121.64198
## 50.00000 100.31996 117.56451
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 4.834458 4.834458 4.834458 4.834458 4.834458 4.834458 4.834458 4.834458
##  [9] 4.834458 4.834458 4.834458 4.834458 4.834458 4.834458
## 
## $佐賀県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%       95%
## 48.14286 2.678993 1.5379582
## 48.28571 2.608445 1.4300645
## 48.42857 2.540065 1.3254868
## 48.57143 2.473665 1.2239368
## 48.71429 2.409082 1.1251660
## 48.85714 2.346175 1.0289579
## 49.00000 2.284820 0.9351228
## 49.14286 2.224906 0.8434933
## 49.28571 2.166338 0.7539209
## 49.42857 2.109028 0.6662729
## 49.57143 2.052899 0.5804304
## 49.71429 1.997880 0.4962861
## 49.85714 1.943908 0.4137432
## 50.00000 1.890925 0.3327135
## 
## $佐賀県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 6.989923 8.130957
## 48.28571 7.060471 8.238851
## 48.42857 7.128851 8.343429
## 48.57143 7.195251 8.444979
## 48.71429 7.259833 8.543750
## 48.85714 7.322740 8.639958
## 49.00000 7.384096 8.733793
## 49.14286 7.444009 8.825422
## 49.28571 7.502577 8.914995
## 49.42857 7.559887 9.002643
## 49.57143 7.616017 9.088485
## 49.71429 7.671036 9.172629
## 49.85714 7.725008 9.255172
## 50.00000 7.777990 9.336202
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 0.8137025 1.1109724 0.6858755 1.5235648 0.7789296 1.1471284 0.9634231
##  [8] 0.9303393 0.8624731 0.9383183 1.2973738 0.9634698 1.0478970 1.0931456
## 
## $長崎県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%       95%
## 48.14286 -1.285478 -2.396718
## 48.28571 -1.218392 -2.451482
## 48.42857 -2.126067 -3.614620
## 48.57143 -1.566678 -3.202554
## 48.71429 -2.627811 -4.431231
## 48.85714 -2.519157 -4.459971
## 49.00000 -2.959625 -5.036361
## 49.14286 -3.370530 -5.647273
## 49.28571 -3.716802 -6.140924
## 49.42857 -3.936364 -6.516864
## 49.57143 -3.839406 -6.558654
## 49.71429 -4.430735 -7.286254
## 49.85714 -4.587976 -7.571428
## 50.00000 -4.776451 -7.883628
## 
## $長崎県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%       95%
## 48.14286 2.912883  4.024123
## 48.28571 3.440336  4.673427
## 48.42857 3.497818  4.986371
## 48.57143 4.613808  6.249683
## 48.71429 4.185670  5.989090
## 48.85714 4.813414  6.754228
## 49.00000 4.886471  6.963208
## 49.14286 5.231209  7.507951
## 49.28571 5.441748  7.865870
## 49.42857 5.813000  8.393501
## 49.57143 6.434154  9.153401
## 49.71429 6.357674  9.213194
## 49.85714 6.683771  9.667222
## 50.00000 6.962742 10.069919
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 12.06734 15.00168 13.49327 15.95461 12.15563 16.43749 12.93302 14.70185
##  [9] 14.92375 13.18867 15.49996 13.59559 14.43317 14.80810
## 
## $熊本県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 7.504842 5.089602
## 48.28571 9.173229 6.087836
## 48.42857 7.522543 4.361831
## 48.57143 9.696261 6.383290
## 48.71429 5.601918 2.132594
## 48.85714 9.609201 5.994524
## 49.00000 5.920196 2.207830
## 49.14286 7.336790 3.437963
## 49.28571 7.412187 3.435806
## 49.42857 5.386653 1.256515
## 49.57143 7.491508 3.252091
## 49.71429 5.401750 1.064194
## 49.85714 5.978053 1.502182
## 50.00000 6.196078 1.637149
## 
## $熊本県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 16.62984 19.04508
## 48.28571 20.83012 23.91552
## 48.42857 19.46400 22.62471
## 48.57143 22.21296 25.52593
## 48.71429 18.70934 22.17866
## 48.85714 23.26577 26.88045
## 49.00000 19.94585 23.65821
## 49.14286 22.06691 25.96573
## 49.28571 22.43531 26.41170
## 49.42857 20.99069 25.12083
## 49.57143 23.50841 27.74782
## 49.71429 21.78943 26.12699
## 49.85714 22.88830 27.36417
## 50.00000 23.42012 27.97905
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 16.11938 16.39203 17.25159 15.98085 17.59423 17.48387 16.66256 17.52830
##  [9] 17.45796 17.77457 17.60550 16.69406 16.49712 17.95733
## 
## $大分県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 13.90672 12.73541
## 48.28571 13.95109 12.65893
## 48.42857 14.60196 13.19933
## 48.57143 13.13781 11.63279
## 48.71429 14.57012 12.96926
## 48.85714 14.28895 12.59765
## 49.00000 13.30549 11.52836
## 49.14286 14.07475 12.24656
## 49.28571 13.88186 11.98878
## 49.42857 14.07998 12.12418
## 49.57143 13.79609 11.77952
## 49.71429 12.77321 10.69764
## 49.85714 12.46790 10.33496
## 50.00000 13.82258 11.63378
## 
## $大分県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 18.33205 19.50336
## 48.28571 18.83298 20.12514
## 48.42857 19.90122 21.30385
## 48.57143 18.82389 20.32890
## 48.71429 20.61834 22.21920
## 48.85714 20.67880 22.37009
## 49.00000 20.01962 21.79675
## 49.14286 20.98185 22.81005
## 49.28571 21.03406 22.92714
## 49.42857 21.46917 23.42497
## 49.57143 21.41490 23.43148
## 49.71429 20.61491 22.69049
## 49.85714 20.52634 22.65927
## 50.00000 22.09207 24.28087
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 5.654054 5.273337 5.273337 5.273337 5.273337 5.273337 5.273337 5.273337
##  [9] 5.273337 5.273337 5.273337 5.273337 5.273337 5.273337
## 
## $宮崎県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                  80%        95%
## 48.14286  2.59901412  0.9817738
## 48.28571  2.05508444  0.3514445
## 48.42857  1.73427098 -0.1391973
## 48.57143  1.44021464 -0.5889177
## 48.71429  1.16716290 -1.0065143
## 48.85714  0.91116959 -1.3980223
## 49.00000  0.66938835 -1.7677948
## 49.14286  0.43968601 -2.1190942
## 49.28571  0.22041501 -2.4544402
## 49.42857  0.01027142 -2.7758271
## 49.57143 -0.19179777 -3.0848653
## 49.71429 -0.38665741 -3.3828774
## 49.85714 -0.57502819 -3.6709656
## 50.00000 -0.75751818 -3.9500600
## 
## $宮崎県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%      95%
## 48.14286  8.709093 10.32633
## 48.28571  8.491589 10.19523
## 48.42857  8.812403 10.68587
## 48.57143  9.106459 11.13559
## 48.71429  9.379511 11.55319
## 48.85714  9.635504 11.94470
## 49.00000  9.877285 12.31447
## 49.14286 10.106988 12.66577
## 49.28571 10.326259 13.00111
## 49.42857 10.536402 13.32250
## 49.57143 10.738471 13.63154
## 49.71429 10.933331 13.92955
## 49.85714 11.121702 14.21764
## 50.00000 11.304192 14.49673
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 28.52233 21.58438 18.87040 14.29229 11.81386 11.81386 11.81386 11.81386
##  [9] 11.81386 11.81386 11.81386 11.81386 11.81386 11.81386
## 
## $鹿児島県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%       95%
## 48.14286 23.945401 21.522523
## 48.28571 16.045820 13.113883
## 48.42857 12.950936  9.817359
## 48.57143  8.092059  4.809856
## 48.71429  5.500743  2.158784
## 48.85714  5.457671  2.092910
## 49.00000  5.414889  2.027480
## 49.14286  5.372390  1.962484
## 49.28571  5.330171  1.897915
## 49.42857  5.288224  1.833763
## 49.57143  5.246545  1.770021
## 49.71429  5.205129  1.706681
## 49.85714  5.163971  1.643735
## 50.00000  5.123067  1.581177
## 
## $鹿児島県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 33.09925 35.52213
## 48.28571 27.12294 30.05488
## 48.42857 24.78987 27.92345
## 48.57143 20.49252 23.77472
## 48.71429 18.12697 21.46893
## 48.85714 18.17004 21.53480
## 49.00000 18.21282 21.60023
## 49.14286 18.25532 21.66523
## 49.28571 18.29754 21.72980
## 49.42857 18.33949 21.79395
## 49.57143 18.38117 21.85769
## 49.71429 18.42258 21.92103
## 49.85714 18.46374 21.98398
## 50.00000 18.50465 22.04653
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##  [1] 33.34028 35.29308 30.23762 32.04605 24.97429 30.19482 33.06779 31.10000
##  [9] 31.10000 31.10000 31.10000 31.10000 31.10000 31.10000
## 
## $沖縄県$lower
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##                80%         95%
## 48.14286 20.700166 14.00889378
## 48.28571 21.643197 14.41738475
## 48.42857 15.647681  7.92423544
## 48.57143 16.573074  8.38217311
## 48.71429  8.666011  0.03292945
## 48.85714 13.091990  4.03829764
## 49.00000 15.205719  5.75010726
## 49.14286 11.142691  0.57792749
## 49.28571 10.054201 -1.08677386
## 49.42857  9.019305 -2.66951160
## 49.57143  8.030788 -4.18131823
## 49.71429  7.082922 -5.63095315
## 49.85714  6.171071 -7.02550883
## 50.00000  5.291417 -8.37082375
## 
## $沖縄県$upper
## Time Series:
## Start = c(48, 2) 
## End = c(50, 1) 
## Frequency = 7 
##               80%      95%
## 48.14286 45.98039 52.67166
## 48.28571 48.94297 56.16878
## 48.42857 44.82755 52.55100
## 48.57143 47.51903 55.70993
## 48.71429 41.28257 49.91565
## 48.85714 47.29765 56.35135
## 49.00000 50.92987 60.38548
## 49.14286 51.05732 61.62208
## 49.28571 52.14581 63.28678
## 49.42857 53.18070 64.86952
## 49.57143 54.16922 66.38133
## 49.71429 55.11709 67.83096
## 49.85714 56.02894 69.22552
## 50.00000 56.90859 70.57083